Legal Document Classification For A Leading LPO Using AI
About the Client
Legal Process Outsourcing company in the US. One of the top ranked LPOs as per Frost and Sullivan.
Business Requirement
- Daily monitor 2600+ US Federal Tax and Legislation websites
- Download the content and Identify changes in the content
- Manually go through the content and classify as “Relevant” or “Non-Relevant”
- A team of 12 lawyers performing this task daily
Our Solution
- Ellicium developed a bot to scan all the sites
- Python was used to scrape and download the content
- Machine learning algorithm- Naive Bayes was used to classify the documents as relevant and non-relevant
Process Flow
Business Outcomes
- Timely and Real time data was captured
- 6 person weeks of effort saved every week
- Dependency on the SME reduced by 60%
- 80% growth in website analysis